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Fix conflicts in README rewrite #1077

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@pcuenca pcuenca commented Sep 28, 2023

What does this PR do?

My best effort to merge main in #958 :) We should try to agree on a final version soon to prevent additional conflicts in the future.

maximelaboisson and others added 30 commits September 7, 2023 09:17
# What does this PR do?
The
[changes](https://github.com/huggingface/text-generation-inference/pull/986/files#diff-b72e45030214e50c8ff6e3be837057b3f3368b9779fd942ca680f949fe069eafR176)
disabling exllama on old compute had unintended consequences of not
setting `use_exllama` to `False` if `HAS_EXLLAMA` equals `False` **and**
`CAN_EXLLAMA` equals `False`. This fixes this.

## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the
other checks if that's the case).
- [X] Did you read the [contributor
guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
      Pull Request section?
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@OlivierDehaene @Narsil 

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IDK what else to add in this guide, I looked for relevant code in TGI
codebase and saw that it's used in quantization as well (maybe I could
add that?)
# What does this PR do?

Fix __call__ vs forward.

To reproduce error just launch:
TheBloke/WizardLM-Uncensored-Falcon-7B-GPTQ with gptq (it fails because
falcon code uses `__call__` instead for `forward` calls)

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Fixes # (issue)


## Before submitting
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As more and more people begin to use Baichuan's open-source models, the
influence of Baichuan models is growing, especially in China. Many
community members are interested in adding support for Baichuan models
to TGI. Meanwhile, Baichuan is a very open company, and in the future,
it plans to open-source more and more models, taking all this into
consideration, we would like to add support for the Baichuan model to
TGI. To do this, we need to make some changes, which we hope can be
merged into the main branch of TGI. In the future, we would be happy to
help maintain support for Baichuan models in TGI. We sincerely hope that
our pull request can be accepted. Thank you.

By the way, the changes of this time mainly for supporting Baichuan-7B.

---------

Co-authored-by: xiaoyuze <[email protected]>
Co-authored-by: Nicolas Patry <[email protected]>
Co-authored-by: Nicolas Patry <[email protected]>
Co-authored-by: Omar Sanseviero <[email protected]>
Co-authored-by: Pedro Cuenca <[email protected]>
Co-authored-by: Nicolas Patry <[email protected]>
Co-authored-by: Pedro Cuenca <[email protected]>
Co-authored-by: Omar Sanseviero <[email protected]>
Co-authored-by: Mishig <[email protected]>
Co-authored-by: Pedro Cuenca <[email protected]>
Co-authored-by: OlivierDehaene <[email protected]>
if there's no cuda. disable custom kernels

# What does this PR do?

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Signed-off-by: Wang, Yi A <[email protected]>
# What does this PR do?

Fixes huggingface#1004

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# What does this PR do?

Fixes huggingface#1038

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…e#1054)

# Add AWQ quantization inference support

Fixes
huggingface#781

This PR (partially) adds support for AWQ quantization for inference.
More information on AWQ [here](https://arxiv.org/abs/2306.00978). In
general, AWQ is faster and more accurate than GPTQ, which is currently
supported by TGI.

This PR installs 4-bit GEMM custom CUDA kernels released by AWQ authors
(in `requirements.txt`, just one line change).

Quick way to test this PR would be bring up TGI as follows:

```
text-generation-server download-weights abhinavkulkarni/codellama-CodeLlama-7b-Python-hf-w4-g128-awq

text-generation-launcher \
--huggingface-hub-cache ~/.cache/huggingface/hub/ \
--model-id abhinavkulkarni/codellama-CodeLlama-7b-Python-hf-w4-g128-awq \
--trust-remote-code --port 8080 \
--max-input-length 2048 --max-total-tokens 4096 --max-batch-prefill-tokens 4096 \
--quantize awq
```

Please note:
* This PR was tested with FlashAttention v2 and vLLM.
* This PR adds support for AWQ inference, not quantizing the models.
That needs to be done outside of TGI, instructions

[here](https://github.com/mit-han-lab/llm-awq/tree/f084f40bd996f3cf3a0633c1ad7d9d476c318aaa).
* This PR only adds support for `FlashLlama` models for now.
* Multi-GPU setup has not been tested. 
* No integration tests have been added so far, will add later if
maintainers are interested in this change.
* This PR can be tested on any of the models released

[here](https://huggingface.co/abhinavkulkarni?sort_models=downloads#models).

Please refer to the linked issue for benchmarks for

[abhinavkulkarni/meta-llama-Llama-2-7b-chat-hf-w4-g128-awq](https://huggingface.co/abhinavkulkarni/meta-llama-Llama-2-7b-chat-hf-w4-g128-awq)
vs

[TheBloke/Llama-2-7b-Chat-GPTQ](https://huggingface.co/TheBloke/Llama-2-7b-Chat-GPTQ).

Please note, AWQ has released faster (and in case of Llama, fused)
kernels for 4-bit GEMM, currently at the top of the `main` branch at
https://github.com/mit-han-lab/llm-awq, but this PR uses an older commit
that has been tested to work. We can switch to latest commit later on.

## Who can review?

@OlivierDehaene OR @Narsil

---------



# What does this PR do?

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---------

Co-authored-by: Abhinav M Kulkarni <[email protected]>
Co-authored-by: Abhinav Kulkarni <[email protected]>
# What does this PR do?

Fixes huggingface#1056

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Support local config file to avoid unexpected `discard_names`, which
causes huggingface#1057.

In the case of launching local mode without `model.safetensors` file,
the original code will result `discard_names = []` when
`hf_hub_download` throws an connection error.
```python
# server/text_generation_server/cli.py
    try:
        import transformers
        import json
    
    
        config_filename = hf_hub_download(model_id, revision=revision, filename="config.json")
        with open(config_filename, "r") as f:
            config = json.load(f)
        architecture = config["architectures"][0]
    
        class_ = getattr(transformers, architecture)
    
        # Name for this varible depends on transformers version.
        discard_names = getattr(class_, "_tied_weights_keys", [])
        discard_names.extend(getattr(class_, "_keys_to_ignore_on_load_missing", []))
    
    except Exception as e:
        discard_names = []
```

The expected `_tied_weights_keys` of OPT-1.3b is `["lm_head.weight"]`,
and its tied weight `"model.decoder.embed_tokens.weight"` will be kept
in the safetensors conversion. But the above empty `discard_names` will
lead to `"lm_head.weight"` being kept and
`"model.decoder.embed_tokens.weight"` being discard in the subsequent
method `_remove_duplicate_names`, which causes error huggingface#1057.

So add a local mode branch to get the expected `discard_names` like
follows. This modification also applies to other models

```python
# server/text_generation_server/cli.py
        if is_local_model:
            config_filename = os.path.join(model_id, "config.json")
        else:
            config_filename = hf_hub_download(model_id, revision=revision, filename="config.json")
```


In addition, when `_tied_weights_keys` or
`_keys_to_ignore_on_load_missing` is `None`, the above code will also
throw an error unexpectedly. This is fixed in PR huggingface#1052


## Before submitting
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other checks if that's the case).
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      Pull Request section?
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Here are the
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N/A
- [ ] Did you write any new necessary tests?  N/A


## Who can review?

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@Narsil
# What does this PR do?

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Model Class attributes `_tied_weights_keys`, `
_keys_to_ignore_on_load_missing` can only be `None` or a List.
`getattr(class_, "_keys_to_ignore_on_load_missing", [])` will return
`None` if `_keys_to_ignore_on_load_missing` is None, and
`discard_names.extend(None)` will trigger an exception, even though
`_tied_weights_keys` exists.

## Who can review?

@OlivierDehaene  @Narsil

---------

Co-authored-by: Nicolas Patry <[email protected]>
…gface#1033)

# What does this PR do?

Install curl within base image, negligible regarding the image volume
and will allow to easily perform a better health check. Not sure about
the failing github actions though. Should I fix something ?

Signed-off-by: Raphael <[email protected]>
Co-authored-by: Raphael <[email protected]>
…#1023)

# What does this PR do?

I made an embarrassing mistake where I accidentally passed normal
softmax probabilities into `batch_top_tokens` for `CausalLM` and
`Seq2SeqLM`.

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@Narsil
# What does this PR do?

Upgrade all relevant versions and dependencies.

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Added note on serving supported models from a different folder without
re-downloading them.

---------

Co-authored-by: Nicolas Patry <[email protected]>
# What does this PR do?

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Fixes # (issue)


## Before submitting
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other checks if that's the case).
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      to it if that's the case.
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---------

Co-authored-by: zhaosida <[email protected]>
… tokenizers might do). (huggingface#1065)

Superseed huggingface#1024


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      to it if that's the case.
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---------

Co-authored-by: bangoz <[email protected]>
…gingface#1060)

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`FastLinear.load` requires 4 parameters, but in the following only 3 are
given. This PR fix this.

```python
# server/text_generation_server/models/custom_modeling/opt_modeling.py
        if config.word_embed_proj_dim != config.hidden_size:
            self.project_out = FastLinear.load(
                config, prefix="model.decoder.project_out", bias=False
            )
        else:
            self.project_out = None

        if config.word_embed_proj_dim != config.hidden_size:
            self.project_in = FastLinear.load(
                config, prefix="model.decoder.project_in", bias=False
```

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I had to open this PR since I initially worked from my fork, and it
requires a handful of work to trigger a new github action on my fork's
specific branch (couldn't find a way, at least, despite trying all of
them).

---------

Co-authored-by: Nicolas Patry <[email protected]>
Adding a new line to escape between heading and codeblock. However, it
is a hotfix and I will work on a permanent solution on
https://github.com/huggingface/doc-builder
@pcuenca pcuenca requested a review from osanseviero September 28, 2023 16:53
@pcuenca
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pcuenca commented Sep 28, 2023

this doesn't look right, closing

@pcuenca pcuenca closed this Sep 28, 2023
@HuggingFaceDocBuilderDev

The docs for this PR live here. All of your documentation changes will be reflected on that endpoint.

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